医疗保健中失效模式的算法预测。
Algorithmic prediction of failure modes in healthcare.
机构信息
The Israel Center for the of Failure Modes in Medical Systems, Program of Emergency Medicine, Zefat Academic College, Jrusalem St. 11, Safed 13206, Israel.
Hospital Management, Quality and Safety Department, Sheba Medical Center, Ramat Gan, Israel.
出版信息
Int J Qual Health Care. 2021 Feb 20;33(1). doi: 10.1093/intqhc/mzaa151.
BACKGROUND
Preventing medical errors is crucial, especially during crises like the COVID-19 pandemic. Failure Modes and Effects Analysis (FMEA) is the most widely used prospective hazard analysis in healthcare. FMEA relies on brainstorming by multi-disciplinary teams to identify hazards. This approach has two major weaknesses: significant time and human resource investments, and lack of complete and error-free results.
OBJECTIVES
To introduce the algorithmic prediction of failure modes in healthcare (APFMH) and to examine whether APFMH is leaner in resource allocation in comparison to the traditional FMEA and whether it ensures the complete identification of hazards.
METHODS
The patient identification during imaging process at the emergency department of Sheba Medical Center was analyzed by FMEA and APFMH, independently and separately. We compared between the hazards predicted by APFMH method and the hazards predicted by FMEA method; the total participants' working hours invested in each process and the adverse events, categorized as 'patient identification', before and after the recommendations resulted from the above processes were implemented.
RESULTS
APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA: the former used 21 h whereas the latter required 63 h. Following the implementation of the recommendations, the adverse events decreased by 44% annually (P = 0.0026). Most adverse events were preventable, had all recommendations been fully implemented.
CONCLUSION
In light of our initial and limited-size study, APFMH is more effective in identifying hazards (P < 0.0001) and is leaner in resources than the traditional FMEA. APFMH is suggested as an alternative to FMEA since it is leaner in time and human resources, ensures more complete hazard identification and is especially valuable during crisis time, when new protocols are often adopted, such as in the current days of the COVID-19 pandemic.
背景
预防医疗差错至关重要，尤其是在 COVID-19 大流行等危机期间。失效模式与影响分析（FMEA）是医疗保健中应用最广泛的前瞻性危害分析方法。FMEA 依赖多学科团队的头脑风暴来识别危害。这种方法有两个主要弱点：需要大量的时间和人力资源投入，并且无法保证结果完整无错误。
目的
介绍医疗保健失效模式的算法预测（APFMH），并检验 APFMH 在资源分配方面是否比传统 FMEA 更精简，以及是否能确保全面识别危害。
方法
对舍巴医疗中心急诊科的影像过程中的患者身份识别分别采用 FMEA 和 APFMH 进行分析。我们比较了 APFMH 方法预测的危害与 FMEA 方法预测的危害之间的差异；比较了每个过程中总参与者投入的工作时间以及实施上述过程的建议后，归类为“患者身份识别”的不良事件。
结果
APFMH 在识别危害方面更有效（P<0.0001），并且比传统 FMEA 更节省资源：前者用时 21 小时，而后者需要 63 小时。实施建议后，不良事件每年减少 44%（P=0.0026）。如果所有建议都得到充分实施，大多数不良事件是可以预防的。
结论
根据我们的初步和有限规模的研究，APFMH 在识别危害方面更有效（P<0.0001），并且比传统 FMEA 更节省资源。APFMH 可作为 FMEA 的替代方法，因为它在时间和人力资源方面更精简，能更全面地识别危害，尤其是在危机时期（如当前的 COVID-19 大流行期间），新协议经常被采用时，具有更高的价值。
Zotero 插件